A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Clusters, outliers, and regression: fixed point clusters
Journal of Multivariate Analysis
Principal component analysis for data containing outliers and missing elements
Computational Statistics & Data Analysis
Linear grouping using orthogonal regression
Computational Statistics & Data Analysis
Multiple model regression estimation
IEEE Transactions on Neural Networks
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Robust fitting of mixture regression models
Computational Statistics & Data Analysis
Benchmark testing of algorithms for very robust regression: FS, LMS and LTS
Computational Statistics & Data Analysis
Robust mixture regression using the t-distribution
Computational Statistics & Data Analysis
Robust clustering around regression lines with high density regions
Advances in Data Analysis and Classification
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The presence of clusters in a data set is sometimes due to the existence of certain relations among the measured variables which vary depending on some hidden factors. In these cases, observations could be grouped in a natural way around linear and nonlinear structures and, thus, the problem of doing robust clustering around linear affine subspaces has recently been tackled through the minimization of a trimmed sum of orthogonal residuals. This ''orthogonal approach'' implies that there is no privileged variable playing the role of response variable or output. However, there are problems where clearly one variable is wanted to be explained in terms of the other ones and the use of vertical residuals from classical linear regression seems to be more advisable. The so-called TCLUST methodology is extended to perform robust clusterwise linear regression and a feasible algorithm for the practical implementation is proposed. The algorithm includes a ''second trimming'' step aimed to diminishing the effect of leverage points.